Bagian I Petunjuk pengisian :
Isilah pertanyaan-pertanyaan yang ada di bawah ini sesuai dengan jawaban anda.
1. Apakah jenis kelamin anda ? a. Pria
b. Wanita 2. Tingkat pendidikan anda saat ini adalah :
a. SD b. SMP
c. SMA d. Lainnya __________
3. Dari mana anda mengetahui Melodia ? a. Informasi dari teman atau kerabat
b. Flyer atau selebaran c. Koran
d. Majalah e. Lainnya __________
4. Grade berapakah anda saat ini ? a. 3
b. 4 c. 5
d. 5 5. Sudah berapa lama anda kursus piano di Melodia?
a. 2 tahun b. 2-4 tahun
c. 4-6 tahun d. 6 tahun
Untuk waktu kedepan,apakah anda masih bersedia untuk melanjutkan studi di Melodia ?
a. Pasti melanjutkan b. Mungkin akan melanjutkan
c. Mungkin tidak akan melanjutkan d. Pasti tidak akan melanjutkan
LAMPIRAN 2 REKAP KUESIONER
1 2
3 4
5 6
7 8
9 10
11 12
13 14
15 16
17 18
19 20
21 22
23 24
25 26
27 1
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 2
2 3
3 3
2 3
3 2
3 2
3 3
3 3
2 3
3 3
2 2
2 2
3 3
4 3
2 3
3 3
2 2
3 3
3 3
3 3
3 3
3 4
4 3
3 3
3 2
2 3
3 3
1 1
1 4
3 4
3 2
2 4
4 1
2 1
4 4
4 4
4 4
4 4
4 3
4 2
3 4
4 4
1 5
3 2
1 1
1 2
3 3
3 2
3 2
3 3
4 4
3 3
2 3
3 2
3 4
3 2
3 6
3 3
3 2
3 3
3 2
2 2
2 3
3 3
3 3
3 3
3 2
3 3
3 3
3 4
3 7
3 3
3 3
3 3
3 3
3 3
3 4
4 4
3 3
3 3
3 3
3 3
3 3
3 3
2 8
3 3
4 3
3 3
2 3
3 2
3 4
3 3
3 3
3 3
2 2
3 3
3 3
3 3
3 9
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
4 10
3 3
3 3
2 3
2 3
3 2
3 4
3 3
3 3
3 3
3 2
3 3
3 3
3 3
2 11
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 12
3 3
3 3
2 3
2 3
3 3
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 13
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
3 14
4 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
3 15
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 16
3 3
3 3
3 3
2 3
3 2
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 17
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
2 18
4 4
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 19
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 20
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 21
3 3
3 3
3 3
3 3
3 2
3 4
4 3
3 3
3 3
2 2
3 3
3 3
3 3
2 22
4 4
3 3
4 4
3 4
3 4
3 4
3 4
4 3
3 4
4 3
3 4
3 3
2 2
2 23
4 4
3 4
4 4
3 3
4 3
4 4
3 4
3 3
4 3
4 3
3 3
3 4
3 3
3 24
4 3
4 3
3 3
3 4
3 2
4 4
4 4
4 4
3 4
3 4
4 4
3 3
3 3
3 25
3 4
3 4
3 3
3 4
4 4
3 4
3 4
3 3
4 4
3 4
3 4
3 4
4 4
3 26
3 4
4 4
4 4
4 3
3 4
4 3
3 4
4 3
3 3
4 4
4 4
3 4
4 4
4 27
3 3
3 3
3 4
4 4
4 4
4 3
3 4
3 4
3 4
4 4
4 4
4 3
3 4
3 28
3 4
3 4
3 3
3 3
3 3
3 3
4 4
4 3
4 3
4 3
4 3
4 3
3 4
4 Responden
Skala Kepentingan
29 3
3 3
4 4
3 4
3 4
3 4
3 3
4 3
3 3
4 4
4 3
4 3
4 3
3 4
30 3
3 4
3 4
3 4
3 3
4 4
4 3
3 3
4 3
4 3
4 3
4 3
4 4
4 3
31 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
32 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
33 3
4 3
3 3
4 3
3 3
2 3
4 3
3 3
3 4
3 3
3 3
3 3
3 3
3 3
34 3
4 3
3 3
3 3
2 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
35 3
4 3
3 3
3 2
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
36 3
3 3
3 3
3 2
2 3
3 2
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
37 3
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 2
3 3
3 3
3 3
2 3
38 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
39 3
4 3
3 3
4 3
3 3
4 3
3 3
3 4
3 3
4 3
3 3
3 3
3 3
3 3
40 3
3 3
3 3
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 3
3 3
41 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
4 3
3 3
3 3
3 3
3 3
3 3
42 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
43 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
44 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
45 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
46 3
3 3
3 3
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 3
3 3
47 3
3 3
3 3
3 2
3 2
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 3
3 3
48 2
3 3
2 3
3 2
3 3
3 2
3 3
3 3
3 4
3 3
3 3
3 3
3 3
3 3
49 3
3 3
4 4
4 3
4 3
4 3
4 3
4 3
3 3
3 3
4 4
4 3
4 3
3 4
50 3
3 3
3 4
4 4
4 3
3 3
4 3
4 3
4 3
4 3
4 4
3 3
4 3
3 3
51 3
4 3
3 3
4 3
4 3
4 3
4 3
3 3
4 3
3 4
3 4
3 4
3 3
4 3
52 4
4 4
3 3
3 4
3 4
4 4
3 3
3 3
3 4
4 3
4 4
4 3
3 4
3 3
53 3
4 4
4 4
3 4
3 4
3 4
4 4
4 3
3 4
4 4
4 4
3 3
4 4
4 4
54 4
3 4
3 4
4 4
4 3
4 3
4 4
3 4
3 3
4 2
4 4
3 4
3 3
3 2
55 4
4 4
4 2
2 2
4 4
4 4
3 3
3 4
3 4
3 3
4 3
4 3
3 3
4 4
56 3
3 3
3 3
3 3
3 3
3 3
3 4
4 4
3 4
4 3
3 3
4 4
3 3
3 3
57 3
4 3
3 3
2 2
3 3
3 2
4 3
2 4
2 3
3 3
2 3
4 4
4 3
4 3
1 2
3 4
5 6
7 8
9 10
11 12
13 14
15 16
17 18
19 20
21 22
23 24
25 26
27 1
3 3
4 3
3 2
3 3
3 3
3 3
3 3
3 3
3 2
2 3
3 3
3 3
3 3
3 2
3 3
3 3
4 3
3 2
3 2
1 3
3 4
3 3
3 2
3 3
2 3
2 3
1 3
1 3
3 3
2 2
3 3
3 2
3 2
2 3
2 1
1 3
3 3
2 1
2 3
3 2
1 1
1 4
3 3
4 1
2 3
2 1
4 4
4 4
4 4
4 3
3 3
3 1
1 4
2 3
2 4
1 5
3 2
2 3
3 4
3 2
1 3
4 3
2 4
3 1
3 3
3 3
2 3
4 3
2 3
3 6
3 3
3 3
3 3
3 3
3 3
4 4
4 4
4 4
4 3
3 3
3 3
3 3
3 3
3 7
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 8
3 3
3 3
3 3
3 3
4 2
3 4
3 4
3 3
3 3
2 2
3 3
3 3
2 3
3 9
3 3
3 3
4 3
4 3
3 3
3 3
4 3
3 3
3 3
2 2
3 3
3 3
3 3
3 10
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 11
3 3
3 3
3 3
2 3
3 2
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
3 4
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 2
2 3
3 3
3 3
3 3
3 13
4 3
3 3
2 3
2 3
3 2
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 14
3 3
3 2
3 3
3 3
3 2
3 4
4 4
3 3
3 3
2 3
3 3
3 3
3 3
3 15
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 16
3 3
3 3
3 3
2 3
4 2
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
3 17
4 4
3 3
3 3
3 3
3 3
3 4
4 3
3 3
3 3
2 3
3 3
3 3
3 3
3 18
3 3
3 3
3 3
3 4
4 2
3 4
4 3
3 3
3 3
3 2
3 3
3 3
3 3
3 19
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 20
3 3
3 2
3 3
3 3
3 2
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 21
3 3
3 3
3 3
3 3
3 2
3 3
3 3
3 3
3 3
2 2
3 3
3 3
3 3
3 22
4 4
3 3
3 3
4 4
3 3
4 4
3 3
4 3
4 3
4 3
4 3
4 3
4 3
4 23
4 3
3 4
3 3
2 3
4 3
2 3
4 3
3 3
2 4
4 3
3 4
3 4
3 4
4 24
4 4
4 4
3 3
3 4
3 2
4 4
4 4
4 4
4 4
4 3
3 3
4 4
4 4
4 25
4 4
4 4
3 2
2 3
3 3
4 4
4 4
4 4
4 4
4 3
2 3
4 4
4 4
4 26
3 4
4 4
4 4
3 4
4 4
3 3
3 4
4 3
3 4
3 3
4 3
4 4
4 3
4 27
3 3
4 4
3 3
4 3
4 3
4 4
4 3
4 3
3 4
4 3
4 4
3 4
4 4
3 28
3 3
4 4
3 2
2 3
4 3
3 3
2 2
3 3
3 2
3 2
3 2
2 3
2 2
3 Skala Persepsi
Responden
29 3
3 3
4 4
4 3
4 3
4 3
4 3
3 3
4 4
3 4
3 4
3 3
3 4
3 3
30 3
3 3
3 4
4 4
3 3
4 4
3 4
3 4
3 4
4 3
3 3
4 4
3 4
3 4
31 3
3 3
4 3
3 4
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 4
3 3
4 3
32 3
3 3
3 3
3 2
3 2
2 3
3 3
3 3
3 3
3 3
3 3
3 4
3 3
3 3
33 3
3 3
3 3
3 3
3 3
1 3
3 3
3 3
3 3
3 3
4 4
4 3
3 3
3 3
34 4
4 3
4 3
4 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
2 3
3 3
35 3
3 3
4 3
3 4
3 3
3 3
4 3
2 2
3 3
3 3
3 3
3 3
3 3
3 3
36 3
3 3
3 3
2 3
2 3
2 3
2 2
3 3
4 4
4 2
3 3
3 3
3 3
3 3
37 3
3 3
3 3
3 3
3 3
3 3
4 4
4 3
3 3
3 3
3 3
3 3
3 3
3 4
38 3
3 3
3 3
3 3
3 4
3 2
3 3
3 2
3 4
3 3
3 3
3 3
3 3
3 3
39 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
4 3
4 3
3 3
3 3
3 3
40 3
3 3
3 3
3 3
3 4
2 3
4 3
4 3
3 3
3 2
3 3
3 3
3 4
3 3
41 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
42 3
3 3
3 4
4 3
3 3
3 2
3 3
3 3
3 4
4 3
3 3
3 3
3 3
3 3
43 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
44 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
45 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
46 3
3 3
3 3
3 3
3 3
2 3
3 3
3 3
3 3
4 4
3 3
3 3
3 3
3 3
47 4
4 4
3 3
4 4
4 3
2 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
48 3
3 3
3 3
3 3
3 3
3 4
3 2
3 3
3 2
2 3
3 3
3 3
3 3
3 3
49 3
3 4
3 4
3 3
4 4
3 4
3 3
4 3
3 4
3 4
3 3
4 4
4 3
4 3
50 3
3 3
4 4
3 4
3 3
4 4
3 3
3 4
3 4
3 4
3 3
3 4
4 3
4 3
51 4
4 4
3 3
4 4
4 3
4 3
4 3
4 3
4 4
4 3
3 4
3 4
4 3
3 4
52 3
3 4
4 4
3 3
3 4
3 4
4 4
3 4
3 3
4 3
4 4
3 4
3 3
4 4
53 4
4 4
4 3
3 3
4 3
4 3
3 4
3 4
3 3
3 4
4 3
3 4
3 4
3 4
54 3
3 2
2 3
3 2
3 4
4 4
3 2
3 4
4 4
4 4
3 2
3 3
3 3
2 2
55 4
4 4
4 4
4 4
3 3
4 4
4 4
4 4
4 4
4 4
3 3
3 4
4 4
4 4
56 4
4 4
4 3
3 3
4 3
3 3
4 4
4 4
4 4
4 4
4 4
4 4
4 4
4 4
57 3
3 3
2 2
3 3
2 3
4 4
4 3
3 4
4 3
2 3
4 3
3 3
2 3
3 4
LAMPIRAN 3 DATA RESPONDEN
pria wanita
SD SMP
SMA lainnya
informasi teman flyer
koran majalah lainnya
1 1
1 1
2 1
1 1
3 1
1 1
4 1
1 1
5 1
1 1
6 1
1 1
7 1
1 1
8 1
1 1
9 1
1 1
10 1
1 1
11 1
1 1
12 1
1 1
13 1
1 1
14 1
1 1
15 1
1 1
16 1
1 1
17 1
1 1
18 1
1 1
19 1
1 1
20 1
1 1
21 1
1 1
22 1
1 1
23 1
1 1
24 1
1 1
25 1
1 1
26 1
1 1
27 1
1 1
28 1
1 1
29 1
1 1
30 1
1 1
31 1
1 1
32 1
1 1
33 1
1 1
34 1
1 1
35 1
1 1
36 1
1 1
37 1
1 1
38 1
1 1
39 1
1 1
40 1
1 1
41 1
1 1
42 1
1 1
43 1
1 1
44 1
1 1
45 1
1 1
46 1
1 1
47 1
1 1
48 1
1 1
49 1
1 1
50 1
1 1
51 1
1 1
52 1
1 1
53 1
1 1
54 1
1 1
55 1
1 1
56 1
1 1
57 1
1 1
17 40
4 18
17 18
44 8
3 2
3 4
5 5
2tahun 2-4 tahun 4-6 tahun 6 tahun 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
19 16
12 10
15 12
22 8
LAMPIRAN 4 ANALISIS FAKTOR
Total Variance Explained
10.335 41.339
41.339 10.335
41.339 41.339
2.677 10.710
52.049 2.677
10.710 52.049
2.144 8.574
60.623 2.144
8.574 60.623
1.826 7.304
67.927 1.826
7.304 67.927
1.483 5.934
73.861 1.483
5.934 73.861
1.291 5.165
79.025 1.291
5.165 79.025
1.034 4.135
83.161 1.034
4.135 83.161
.890 3.561
86.721 .733
2.930 89.652
.649 2.597
92.249 .468
1.871 94.120
.424 1.697
95.817 .275
1.101 96.917
.258 1.031
97.948 .203
.810 98.758
.122 .490
99.248 .088
.351 99.599
.048 .193
99.792 .027
.107 99.899
.014 .056
99.955 .010
.038 99.993
.002 .007
100.000 2.523E-16
1.009E-15 100.000
2.113E-16 8.452E-16
100.000 -3.94E-16
-1.575E-15 100.000
Component 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
22 23
24 25
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Total Variance Explained
8.400 39.999
39.999 8.400
39.999 39.999
2.636 12.555
52.554 2.636
12.555 52.554
2.002 9.531
62.085 2.002
9.531 62.085
1.688 8.038
70.123 1.688
8.038 70.123
1.200 5.717
75.839 1.200
5.717 75.839
1.094 5.210
81.049 1.094
5.210 81.049
.883 4.207
85.256 .883
4.207 85.256
.688 3.274
88.530 .558
2.658 91.188
.470 2.239
93.427 .379
1.806 95.232
.350 1.666
96.898 .208
.989 97.887
.192 .913
98.801 .120
.570 99.370
.069 .331
99.701 .025
.119 99.820
.019 .091
99.911 .013
.063 99.974
.003 .015
99.989 .002
.011 100.000
Component 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Total Variance Explained
8.167 32.667
32.667 8.167
32.667 32.667
2.539 10.157
42.824 2.539
10.157 42.824
1.887 7.547
50.371 1.887
7.547 50.371
1.532 6.128
56.499 1.532
6.128 56.499
1.432 5.727
62.227 1.432
5.727 62.227
1.299 5.194
67.421 1.299
5.194 67.421
1.125 4.500
71.921 1.125
4.500 71.921
.927 3.709
75.630 .843
3.372 79.002
.714 2.857
81.859 .685
2.741 84.600
.660 2.640
87.240 .614
2.456 89.696
.450 1.799
91.495 .421
1.685 93.180
.317 1.267
94.447 .297
1.186 95.633
.252 1.008
96.641 .212
.847 97.489
.160 .641
98.129 .143
.571 98.701
.128 .511
99.211 .092
.367 99.579
.055 .221
99.800 .050
.200 100.000
Component 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
22 23
24 25
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Total Variance Explained
7.117 33.888
33.888 7.117
33.888 33.888
2.007 9.555
43.443 2.007
9.555 43.443
1.836 8.742
52.185 1.836
8.742 52.185
1.463 6.966
59.151 1.463
6.966 59.151
1.306 6.220
65.371 1.306
6.220 65.371
1.120 5.331
70.702 1.120
5.331 70.702
.987 4.699
75.401 .987
4.699 75.401
.856 4.075
79.477 .683
3.251 82.728
.681 3.244
85.972 .570
2.712 88.684
.429 2.041
90.724 .357
1.698 92.423
.328 1.560
93.982 .306
1.456 95.438
.265 1.261
96.699 .212
1.008 97.707
.174 .830
98.537 .133
.635 99.172
.110 .522
99.694 .064
.306 100.000
Component 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Total Variance Explained
8.167 32.667
32.667 8.167
32.667 32.667
3.913 15.651
15.651 2.539
10.157 42.824
2.539 10.157
42.824 3.226
12.905 28.556
1.887 7.547
50.371 1.887
7.547 50.371
2.837 11.347
39.903 1.532
6.128 56.499
1.532 6.128
56.499 2.361
9.442 49.346
1.432 5.727
62.227 1.432
5.727 62.227
2.009 8.035
57.380 1.299
5.194 67.421
1.299 5.194
67.421 1.833
7.333 64.713
1.125 4.500
71.921 1.125
4.500 71.921
1.802 7.208
71.921 .927
3.709 75.630
.843 3.372
79.002 .714
2.857 81.859
.685 2.741
84.600 .660
2.640 87.240
.614 2.456
89.696 .450
1.799 91.495
.421 1.685
93.180 .317
1.267 94.447
.297 1.186
95.633 .252
1.008 96.641
.212 .847
97.489 .160
.641 98.129
.143 .571
98.701 .128
.511 99.211
.092 .367
99.579 .055
.221 99.800
.050 .200
100.000 Component
1 2
3 4
5 6
7 8
9 10
11 12
13 14
15 16
17 18
19 20
21 22
23 24
25 Total
of Variance Cumulative
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrix
a
.210 .183
.086 .858
-.049 .168
-.034 .240
.222 .161
.845 .116
-.032 .097
.218 .592
.137 .343
.401 -.099
-.187 .567
.069 .013
.223 .594
-.015 -.032
.087 -.034
.137 -.155
.743 .070
.483 .045
-.023 .113
.156 -.039
.253 .792
.296 .121
.073 -.077
.167 -.108
.691 .616
.049 .007
.429 .319
-.050 .207
-.023 .158
.708 .135
.103 -.052
.248 .260
.274 .755
-.159 -.207
-.001 -.095
.065 .675
.237 .227
-.202 -.190
.219 .151
.799 -.010
.205 .012
.174 .114
-.009 .616
.268 .050
.076 .238
.022 .277
.473 .658
.055 .020
.140 -.142
.023 -.043
.611 .219
.270 .171
.217 .193
.051 .231
.270 .182
.673 .128
.223 .112
.493 .203
.291 .361
-.036 .712
.034 .135
-.015 -.035
.194 -.012
.803 .083
-.146 .057
.181 -.075
.275 .023
.421 -.035
-.149 -.074
.720 .091
.542 .030
.428 .176
.183 .297
.189 .217
.414 .223
.177 .592
.372 -.118
.705 .256
.311 .229
.057 .165
.058 .271
.695 .167
-.051 .276
.370 -.143
.803 .219
.216 .239
.050 .035
.022 VAR00001
VAR00002 VAR00003
VAR00004 VAR00005
VAR00006 VAR00007
VAR00008 VAR00010
VAR00011 VAR00012
VAR00013 VAR00014
VAR00015 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00022 VAR00023
VAR00024 VAR00025
VAR00026 VAR00027
1 2
3 4
5 6
7 Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 10 iterations. a.
Total Variance Explained
7.117 33.888
33.888 7.117
33.888 33.888
3.758 17.895
17.895 2.007
9.555 43.443
2.007 9.555
43.443 2.349
11.184 29.079
1.836 8.742
52.185 1.836
8.742 52.185
2.216 10.553
39.632 1.463
6.966 59.151
1.463 6.966
59.151 2.195
10.454 50.086
1.306 6.220
65.371 1.306
6.220 65.371
1.867 8.891
58.977 1.120
5.331 70.702
1.120 5.331
70.702 1.803
8.586 67.562
.987 4.699
75.401 .987
4.699 75.401
1.646 7.839
75.401 .856
4.075 79.477
.683 3.251
82.728 .681
3.244 85.972
.570 2.712
88.684 .429
2.041 90.724
.357 1.698
92.423 .328
1.560 93.982
.306 1.456
95.438 .265
1.261 96.699
.212 1.008
97.707 .174
.830 98.537
.133 .635
99.172 .110
.522 99.694
.064 .306
100.000 Component
1 2
3 4
5 6
7 8
9 10
11 12
13 14
15 16
17 18
19 20
21 Total
of Variance Cumulative
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrix
a
.204 .107
.888 .132
-.028 .038
.105 .237
.233 .853
.159 .121
.070 -.090
.172 .736
.412 -.072
-.028 .209
.096 .524
.586 .124
.166 .129
-.109 -.127
.087 .426
-.279 .432
.605 -.122
-.159 .074
-.241 .219
.183 .782
.002 .196
.284 .101
-.032 -.057
.732 .147
.011 .635
.255 .340
.234 .195
-.135 -.184
-.069 .170
.200 .211
.360 .672
-.036 .234
.033 -.050
.120 -.099
.843 .143
.063 .074
.095 .731
.133 .319
-.141 .226
.044 .179
.733 .028
-.054 .382
.177 .330
.178 .451
.075 .303
.238 .702
.017 .026
.026 .047
.116 .167
.806 .174
.031 -.026
.271 -.167
-.026 .003
.101 -.012
.099 .102
.048 .892
.550 .109
.138 .459
.180 .300
.100 .210
.682 .134
.438 -.022
.063 .295
.711 .167
.237 .269
.019 .271
.132 .250
.601 .053
.073 -.039
.255 .566
.811 .171
.243 .126
.003 .227
.013 VAR00001
VAR00002 VAR00003
VAR00004 VAR00005
VAR00006 VAR00007
VAR00008 VAR00010
VAR00011 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00022 VAR00023
VAR00024 VAR00025
VAR00026 VAR00027
1 2
3 4
5 6
7 Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 9 iterations. a.
Component Matrix
a
.701 -.241
-.221 .005
-.423 -.075
.256 .745
-.230 -.052
-.396 -.249
.163 .202
.765 -.467
.067 -.349
.088 .098
-.098 .709
-.465 -.030
.007 .059
-.212 -.291
.535 .516
.196 -.252
.032 -.132
-.448 .371
.684 .386
-.113 -.028
-.330 .026
.355 .571
.359 -.256
.095 .165
.175 .709
-.286 .235
-.260 .082
-.056 .305
.567 .321
-.243 -.348
-.026 -.457
.085 .385
.359 -.495
.052 .621
.050 -.019
.387 .391
-.486 -.236
.021 .570
.034 .624
.363 -.206
.152 -.486
.270 -.105
.617 -.059
-.385 .245
.045 -.355
.210 .575
.009 .127
.433 .350
.026 .413
.387 -.178
.800 -.162
.109 .203
.051 .224
.374 .386
.672 -.275
.036 .059
.708 .399
-.107 .057
-.088 -.068
.055 .870
-.177 -.069
.080 -.146
-.035 -.184
.814 -.024
.050 .146
.229 .230
-.061 .737
-.200 .010
.476 .071
.109 -.221
.910 -.160
.135 .122
.092 -.044
-.167 VAR00001
VAR00002 VAR00003
VAR00004 VAR00005
VAR00006 VAR00007
VAR00008 VAR00010
VAR00011 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00022 VAR00023
VAR00024 VAR00025
VAR00026 VAR00027
1 2
3 4
5 6
7 Component
Extraction Method: Principal Component Analysis. 7 components extracted.
a.
Total Variance Explained
8.400 39.999
39.999 8.400
39.999 39.999
2.636 12.555
52.554 2.636
12.555 52.554
2.002 9.531
62.085 2.002
9.531 62.085
1.688 8.038
70.123 1.688
8.038 70.123
1.200 5.717
75.839 1.200
5.717 75.839
1.094 5.210
81.049 1.094
5.210 81.049
.883 4.207
85.256 .883
4.207 85.256
.688 3.274
88.530 .558
2.658 91.188
.470 2.239
93.427 .379
1.806 95.232
.350 1.666
96.898 .208
.989 97.887
.192 .913
98.801 .120
.570 99.370
.069 .331
99.701 .025
.119 99.820
.019 .091
99.911 .013
.063 99.974
.003 .015
99.989 .002
.011 100.000
Component 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Component Matrix
a
.692 -.285
-.123 .154
.525 .002
.202 .725
-.274 -.001
.468 .195
.046 -.116
.740 -.481
.066 .321
-.186 -.037
-.149 .688
-.466 .000
-.022 -.086
-.279 .029
.507 .491
.272 .240
-.131 -.323
-.188 .361
.652 .432
.178 -.009
-.031 .368
.337 .529
.393 .312
-.038 .277
.073 .700
-.305 .207
.246 -.193
-.005 .100
.551 .318
-.174 .368
-.047 -.461
.227 .444
.425 -.557
-.125 -.403
.151 .110
.547 -.059
-.241 .295
.198 .545
.319 .733
-.141 .094
-.112 .327
.216 .031
.758 .160
-.124 -.214
-.219 .335
.077 .807
.106 -.293
-.183 -.339
-.098 -.099
.418 .408
-.457 .238
.048 .239
-.486 .609
.349 -.061
-.055 .445
-.143 -.438
.612 -.042
-.317 -.205
.076 -.400
.244 .585
.032 .111
-.455 -.215
.039 .137
.351 -.210
.752 .114
-.326 .153
-.163 .217
.332 .485
-.565 .402
-.037 -.043
.697 .389
-.006 .017
.168 -.063
.103 .860
-.174 -.019
-.034 .086
-.109 -.043
.838 -.016
.043 -.188
-.173 .103
-.203 .763
-.191 .027
-.457 .075
.165 .007
.901 -.165
.171 -.130
-.090 -.094
-.012 VAR00001
VAR00002 VAR00003
VAR00004 VAR00005
VAR00006 VAR00007
VAR00008 VAR00010
VAR00011 VAR00012
VAR00013 VAR00014
VAR00015 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00022 VAR00023
VAR00024 VAR00025
VAR00026 VAR00027
1 2
3 4
5 6
7 Component
Extraction Method: Principal Component Analysis. 7 components extracted.
a.
Total Variance Explained
10.335 41.339
41.339 10.335
41.339 41.339
5.894 23.577
23.577 2.677
10.710 52.049
2.677 10.710
52.049 3.586
14.342 37.920
2.144 8.574
60.623 2.144
8.574 60.623
2.828 11.313
49.233 1.826
7.304 67.927
1.826 7.304
67.927 2.271
9.086 58.319
1.483 5.934
73.861 1.483
5.934 73.861
2.117 8.468
66.787 1.291
5.165 79.025
1.291 5.165
79.025 2.056
8.226 75.013
1.034 4.135
83.161 1.034
4.135 83.161
2.037 8.148
83.161 .890
3.561 86.721
.733 2.930
89.652 .649
2.597 92.249
.468 1.871
94.120 .424
1.697 95.817
.275 1.101
96.917 .258
1.031 97.948
.203 .810
98.758 .122
.490 99.248
.088 .351
99.599 .048
.193 99.792
.027 .107
99.899 .014
.056 99.955
.010 .038
99.993 .002
.007 100.000
2.523E-16 1.009E-15
100.000 2.113E-16
8.452E-16 100.000
-3.94E-16 -1.575E-15
100.000 Component
1 2
3 4
5 6
7 8
9 10
11 12
13 14
15 16
17 18
19 20
21 22
23 24
25 Total
of Variance Cumulative
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotated Component Matrix
a
.501 -.041
-.021 .237
.650 .406
.140 .728
-.055 .139
-.064 .430
.136 .337
.927 .133
.014 -.150
.158 .045
.133 .778
.197 -.123
.089 .055
.326 -.049
.296 .102
.666 .087
-.270 .203
.350 -.025
.116 .911
.184 .086
.136 -.076
.056 .095
.783 .057
.184 -.207
.149 .776
.181 .224
-.057 .193
.060 -.071
.258 .138
.483 -.187
.033 .668
.189 -.114
.830 .147
-.228 .100
.234 .245
.219 .257
.151 -.123
.850 .013
.106 .479
.210 .069
.437 .485
.066 .151
.301 .752
.202 .156
.294 -.003
.142 .442
.736 .081
.036 -.034
.299 .284
.003 .321
.125 -.151
.186 .006
.852 .201
.081 .188
.475 .078
.267 .725
.302 .357
-.051 .178
.103 .693
.023 .309
.599 .109
.371 .001
.075 -.141
.662 .000
.366 .139
-.157 -.505
-.176 -.085
.042 .244
.896 -.069
-.019 .007
.208 .308
.456 .276
.238 .351
.280 .672
.300 .067
.253 .223
.298 .207
.588 .561
.126 .266
.074 .003
.262 .486
.522 -.103
.515 .267
.062 .036
.751 .404
.168 .314
.100 .185
.063 VAR00001
VAR00002 VAR00003
VAR00004 VAR00005
VAR00006 VAR00007
VAR00008 VAR00010
VAR00011 VAR00012
VAR00013 VAR00014
VAR00015 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00022 VAR00023
VAR00024 VAR00025
VAR00026 VAR00027
1 2
3 4
5 6
7 Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 16 iterations. a.
Total Variance Explained
10.335 41.339
41.339 10.335
41.339 41.339
2.677 10.710
52.049 2.677
10.710 52.049
2.144 8.574
60.623 2.144
8.574 60.623
1.826 7.304
67.927 1.826
7.304 67.927
1.483 5.934
73.861 1.483
5.934 73.861
1.291 5.165
79.025 1.291
5.165 79.025
1.034 4.135
83.161 1.034
4.135 83.161
.890 3.561
86.721 .733
2.930 89.652
.649 2.597
92.249 .468
1.871 94.120
.424 1.697
95.817 .275
1.101 96.917
.258 1.031
97.948 .203
.810 98.758
.122 .490
99.248 .088
.351 99.599
.048 .193
99.792 .027
.107 99.899
.014 .056
99.955 .010
.038 99.993
.002 .007
100.000 6.087E-16
2.435E-15 100.000
-2.71E-17 -1.083E-16
100.000 -1.98E-16
-7.917E-16 100.000
Component 1
2 3
4 5
6 7
8 9
10 11
12 13
14 15
16 17
18 19
20 21
22 23
24 25
Total of Variance
Cumulative Total
of Variance Cumulative
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Component Matrix
a
.692 -.285
-.123 .154
.525 .002
.202 .725
-.274 -.001
.468 .195
.046 -.116
.740 -.481
.066 .321
-.186 -.037
-.149 .688
-.466 .000
-.022 -.086
-.279 .029
.507 .491
.272 .240
-.131 -.323
-.188 .361
.652 .432
.178 -.009
-.031 .368
.337 .529
.393 .312
-.038 .277
.073 .700
-.305 .207
.246 -.193
-.005 .100
.551 .318
-.174 .368
-.047 -.461
.227 .444
.425 -.557
-.125 -.403
.151 .110
.418 .408
-.457 .238
.048 .239
-.486 .609
.349 -.061
-.055 .445
-.143 -.438
.612 -.042
-.317 -.205
.076 -.400
.244 .585
.032 .111
-.455 -.215
.039 .137
.351 -.210
.752 .114
-.326 .153
-.163 .217
.332 .485
-.565 .402
-.037 -.043
.697 .389
-.006 .017
.168 -.063
.103 .860
-.174 -.019
-.034 .086
-.109 -.043
.838 -.016
.043 -.188
-.173 .103
-.203 .763
-.191 .027
-.457 .075
.165 .007
.901 -.165
.171 -.130
-.090 -.094
-.012 .547
-.059 -.241
.295 .198
.545 .319
.733 -.141
.094 -.112
.327 .216
.031 .758
.160 -.124
-.214 -.219
.335 .077
.807 .106
-.293 -.183
-.339 -.098
-.099 VAR00001
VAR00002 VAR00003
VAR00004 VAR00005
VAR00006 VAR00007
VAR00008 VAR00010
VAR00011 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00022 VAR00023
VAR00024 VAR00025
VAR00026 VAR00027
VAR00012 VAR00013
VAR00014 VAR00015
1 2
3 4
5 6
7 Component
Extraction Method: Principal Component Analysis. 7 components extracted.
a.
Universitas Kristen Maranatha
LAMPIRAN 5 ANALISIS REGRESI
Universitas Kristen Maranatha •
Percobaan 1
Model Summary
b
.526
a
.276 -.013
.60517 Model
1 R
R Square Adjusted
R Square Std. Error of
the Estimate Predictors: Constant, VAR00027, VAR00005,
VAR00014, VAR00010, VAR00018, VAR00007, VAR00002, VAR00019, VAR00013, VAR00011,
VAR00020, VAR00017, VAR00021, VAR00023, VAR00025, VAR00001
a.
Dependent Variable: VAR00028 b.
ANOVA
b
5.596 16
.350 .955
.519
a
14.649 40
.366 20.246
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, VAR00027, VAR00005, VAR00014, VAR00010, VAR00018, VAR00007, VAR00002, VAR00019, VAR00013, VAR00011, VAR00020, VAR00017,
VAR00021, VAR00023, VAR00025, VAR00001 a.
Dependent Variable: VAR00028 b.
Coefficients
a
4.331 1.169
3.705 .001
1.969 6.694
.075 .399
.052 .189
.851 -.731
.882 -.393
.371 -.280
-1.060 .296
-1.144 .357
-.346 .246
-.268 -1.407
.167 -.843
.151 .069
.171 .066
.402 .690
-.276 .414
.126 .131
.163 .956
.345 -.140
.391 -.073
.188 -.075
-.387 .701
-.453 .308
.057 .176
.057 .326
.746 -.299
.413 -.016
.169 -.016
-.095 .925
-.359 .326
.100 .212
.084 .473
.639 -.329
.530 -.180
.177 -.177
-1.020 .314
-.537 .177
-.243 .150
-.285 -1.618
.114 -.546
.061 .158
.169 .166
.936 .355
-.184 .500
.256 .240
.234 1.065
.293 -.230
.742 .187
.261 .164
.717 .478
-.341 .715
.439 .235
.466 1.871
.069 -.035
.913 -.452
.246 -.506
-1.839 .073
-.949 .045
Constant VAR00001
VAR00002 VAR00005
VAR00007 VAR00010
VAR00011 VAR00013
VAR00014 VAR00017
VAR00018 VAR00019
VAR00020 VAR00021
VAR00023 VAR00025
VAR00027 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Lower Bound Upper Bound
95 Confidence Interval for B
Dependent Variable: VAR00028 a.
Universitas Kristen Maranatha
Model Summary
b
.175
a
.031 -.005
.61220 Model
1 R
R Square Adjusted
R Square Std. Error of
the Estimate Predictors: Constant, VAR00014, VAR00013
a. Dependent Variable: VAR00028
b.
ANOVA
b
.639 2
.319 .852
.432
a
20.238 54
.375 20.877
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, VAR00014, VAR00013 a.
Dependent Variable: VAR00028 b.
Coefficients
a
3.427 .531
6.451 .000
2.362 4.491
-.177 .149
-.174 -1.185
.241 -.477
.122 -.001
.155 -.001
-.009 .993
-.312 .309
Constant VAR00013
VAR00014 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Lower Bound Upper Bound
95 Confidence Interval for B
Dependent Variable: VAR00028 a.
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Model Summary
b
.169
a
.029 -.007
.61284 Model
1 R
R Square Adjusted
R Square Std. Error of
the Estimate Predictors: Constant, VAR00002, VAR00001
a. Dependent Variable: VAR00028
b.
ANOVA
b
.596 2
.298 .794
.457
a
20.281 54
.376 20.877
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, VAR00002, VAR00001 a.
Dependent Variable: VAR00028 b.
Coefficients
a
3.599 .664
5.421 .000
2.268 4.930
-.320 .333
-.216 -.962
.340 -.987
.347 .091
.320 .064
.284 .777
-.551 .733
Constant VAR00001
VAR00002 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Lower Bound Upper Bound
95 Confidence Interval for B
Dependent Variable: VAR00028 a.
Universitas Kristen Maranatha •
Percobaan 3
Model Summary
.497
a
.247 .157
.56068 .247
2.735 6
50 .022
Model 1
R R Square
Adjusted R Square
Std. Error of the Estimate
R Square Change
F Change df1
df2 Sig. F Change
Change Statistics Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 5 for analysis 1 , REGR factor score 4
for analysis 1 , REGR factor score 3 for analysis 1 , REGR factor score 2 for analysis 1 , REGR factor score 1 for analysis 1
a.
ANOVA
b
5.159 6
.860 2.735
.022
a
15.718 50
.314 20.877
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 5 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for
analysis 1 , REGR factor score 2 for analysis 1 , REGR factor score 1 for analysis 1
a.
Dependent Variable: VAR00028 b.
Coefficients
a
2.860 .074
38.507 .000
-.033 .075
-.055 -.446
.658 -.162
.075 -.265
-2.161 .036
.055 .075
.090 .730
.469 .213
.075 .348
2.837 .007
-.020 .075
-.034 -.273
.786 -.127
.075 -.208
-1.698 .096
Constant REGR factor score
1 for analysis 1 REGR factor score
2 for analysis 1 REGR factor score
3 for analysis 1 REGR factor score
4 for analysis 1 REGR factor score
5 for analysis 1 REGR factor score
6 for analysis 1 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Dependent Variable: VAR00028 a.
Universitas Kristen Maranatha
Model Summary
.348
a
.121 .105
.57756 .121
7.586 1
55 .008
.438
b
.192 .162
.55908 .070
4.697 1
54 .035
Model 1
2 R
R Square Adjusted
R Square Std. Error of
the Estimate R Square
Change F Change
df1 df2
Sig. F Change Change Statistics
Predictors: Constant, REGR factor score 4 for analysis 1 a.
Predictors: Constant, REGR factor score 4 for analysis 1 , REGR factor score 2 for analysis 1 b.
Coefficients
a
2.860 .076
37.381 .000
.213 .077
.348 2.754
.008 2.860
.074 38.617
.000 .213
.075 .348
2.845 .006
-.162 .075
-.265 -2.167
.035 Constant
REGR factor score 4 for analysis 1
Constant REGR factor score
4 for analysis 1 REGR factor score
2 for analysis 1 Model
1
2 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Dependent Variable: VAR00028 a.
Model Summary
.497
a
.247 .157
.56068 .247
2.735 6
50 .022
.000
b
.000 .000
.61058 -.247
2.735 6
50 .022
Model 1
2 R
R Square Adjusted
R Square Std. Error of
the Estimate R Square
Change F Change
df1 df2
Sig. F Change Change Statistics
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 5 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for analysis 1 , REGR factor score 2 for analysis 1 , REGR factor score 1 for
analysis 1 a.
Predictor: constant b.
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ANOVA
e
5.159 6
.860 2.735
.022
a
15.718 50
.314 20.877
56 5.136
5 1.027
3.328 .011
b
15.742 51
.309 20.877
56 5.073
4 1.268
4.173 .005
c
15.804 52
.304 20.877
56 4.905
3 1.635
5.426 .002
d
15.972 53
.301 20.877
56 Regression
Residual Total
Regression Residual
Total Regression
Residual Total
Regression Residual
Total Model
1
2 3
4 Sum of
Squares df
Mean Square F
Sig.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 5 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for
analysis 1 , REGR factor score 2 for analysis 1 , REGR factor score 1 for analysis 1
a.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for analysis 1 , REGR factor score 2 for
analysis 1 , REGR factor score 1 for analysis 1 b.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for analysis 1 , REGR factor score 2 for
analysis 1 c.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 2 for analysis 1
d. Dependent Variable: VAR00028
e.
Model Summary
.348
a
.121 .105
.57756 .121
7.586 1
55 .008
.438
b
.192 .162
.55908 .070
4.697 1
54 .035
Model 1
2 R
R Square Adjusted
R Square Std. Error of
the Estimate R Square
Change F Change
df1 df2
Sig. F Change Change Statistics
Predictors: Constant, REGR factor score 4 for analysis 1 a.
Predictors: Constant, REGR factor score 4 for analysis 1 , REGR factor score 2 for analysis 1 b.
Universitas Kristen Maranatha
ANOVA
c
2.530 1
2.530 7.586
.008
a
18.347 55
.334 20.877
56 3.999
2 1.999
6.396 .003
b
16.879 54
.313 20.877
56 Regression
Residual Total
Regression Residual
Total Model
1
2 Sum of
Squares df
Mean Square F
Sig.
Predictors: Constant, REGR factor score 4 for analysis 1 a.
Predictors: Constant, REGR factor score 4 for analysis 1 , REGR factor score 2 for analysis 1
b. Dependent Variable: VAR00028
c.
Model Summary
.497
a
.247 .157
.56068 .247
2.735 6
50 .022
.496
b
.246 .172
.55557 -.001
.075 1
50 .786
.493
c
.243 .185
.55129 -.003
.202 1
51 .655
.485
d
.235 .192
.54896 -.008
.552 1
52 .461
Model 1
2 3
4 R
R Square Adjusted
R Square Std. Error of
the Estimate R Square
Change F Change
df1 df2
Sig. F Change Change Statistics
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 5 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for analysis 1 , REGR factor score 2 for analysis 1 , REGR factor score 1 for
analysis 1 a.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3 for analysis 1 , REGR factor score 2 for analysis 1 , REGR factor score 1 for analysis 1
b. Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 3
for analysis 1 , REGR factor score 2 for analysis 1 c.
Predictors: Constant, REGR factor score 6 for analysis 1 , REGR factor score 4 for analysis 1 , REGR factor score 2 for analysis 1
d.
Universitas Kristen Maranatha •
Percobaan 4
Model Summary
b
.290
a
.084 .050
.59513 Model
1 R
R Square Adjusted
R Square Std. Error of
the Estimate Predictors: Constant, VAR00011, VAR00010
a. Dependent Variable: VAR00028
b.
ANOVA
b
1.751 2
.876 2.472
.094
a
19.126 54
.354 20.877
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, VAR00011, VAR00010 a.
Dependent Variable: VAR00028 b.
Coefficients
a
3.800 .431
8.814 .000
2.936 4.665
-.089 .111
-.113 -.799
.428 -.312
.134 -.222
.139 -.226
-1.591 .117
-.501 .058
Constant VAR00010
VAR00011 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Lower Bound Upper Bound
95 Confidence Interval for B
Dependent Variable: VAR00028 a.
Model Summary
b
.225
a
.051 .016
.60577 Model
1 R
R Square Adjusted
R Square Std. Error of
the Estimate Predictors: Constant, VAR00007, VAR00005
a. Dependent Variable: VAR00028
b.
ANOVA
b
1.061 2
.531 1.446
.244
a
19.816 54
.367 20.877
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, VAR00007, VAR00005 a.
Dependent Variable: VAR00028 b.
Universitas Kristen Maranatha
Coefficients
a
2.307 .594
3.885 .000
1.117 3.498
.315 .189
.240 1.669
.101 -.063
.693 -.143
.151 -.136
-.947 .348
-.445 .160
Constant VAR00005
VAR00007 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Lower Bound Upper Bound
95 Confidence Interval for B
Dependent Variable: VAR00028 a.
Model Summary
b
.270
a
.073 .020
.60434 Model
1 R
R Square Adjusted
R Square Std. Error of
the Estimate Predictors: Constant, VAR00019, VAR00017,
VAR00018 a.
Dependent Variable: VAR00028 b.
ANOVA
b
1.520 3
.507 1.387
.257
a
19.357 53
.365 20.877
56 Regression
Residual Total
Model 1
Sum of Squares
df Mean Square
F Sig.
Predictors: Constant, VAR00019, VAR00017, VAR00018 a.
Dependent Variable: VAR00028 b.
Coefficients
a
3.143 .584
5.382 .000
1.972 4.314
-.204 .184
-.167 -1.110
.272 -.572
.164 .259
.157 .251
1.650 .105
-.056 .574
-.149 .129
-.173 -1.160
.251 -.408
.109 Constant
VAR00017 VAR00018
VAR00019 Model
1 B
Std. Error Unstandardized
Coefficients Beta
Standardized Coefficients
t Sig.
Lower Bound Upper Bound 95 Confidence Interval for B
Dependent Variable: VAR00028 a.
Universitas Kristen Maranatha •
Percobaan 6
Model Summary
.949
a
.901 .359
.55902 .901
1.661 22
4 .335
Model 1
R R Square
Adjusted R Square
Std. Error of the Estimate
R Square Change
F Change df1
df2 Sig. F Change
Change Statistics Predictors: Constant, VAR00027, VAR00017, VAR00022, VAR00006, VAR00012, VAR00019, VAR00020, VAR00021,
VAR00010, VAR00007, VAR00011, VAR00005, VAR00002, VAR00013, VAR00025, VAR00023, VAR00014, VAR00004, VAR00026, VAR00018, VAR00001, VAR00003
a.
Model Summary
.348
a
.121 .105
.57756 .121
7.586 1
55 .008
.438
b
.192 .162
.55908 .070
4.697 1
54 .035
Model 1
2 R
R Square Adjusted
R Square Std. Error of
the Estimate R Square
Change F Change
df1 df2
Sig. F Change Change Statistics
Predictors: Constant, REGR factor score 4 for analysis 1 a.
Predictors: Constant, REGR factor score 4 for analysis 1 , REGR factor score 2 for analysis 1 b.
1-1
BAB 1 PENDAHULUAN
1.1 Latar Belakang Masalah
Musik dewasa ini telah menjadi pilihan yang cukup diminati oleh banyak orang. Orang-orang beranggapan bahwa musik telah memiliki porsi
tersendiri dan mulai dianggap sebagai kebutuhan mereka. Tidak hanya untuk didengarkan, tetapi banyak orang saat ini yang ingin memiliki keterampilan
dalam bermusik. Hal inilah yang melatarbelakangi munculnya begitu banyak tempat kursus alat musik baik secara formal seperti sekolah musik,
perkuliahan jurusan musik dan informal seperti tempat les musik, seminar mengenai keterampilan bermusik, guru privat musik dan lain sebagainya.
Oleh karena itu persaingan dalam industri sekolah musik pun saat ini tidak dapat dihindari.
Para sekolah musik berlomba-lomba untuk mencari konsumen yang berminat untuk mengambil kursus musik di tempat mereka. Saat ini dapat
dilihat begitu banyak iklan-iklan dan selebaran yang beredar dari berbagai sekolah musik untuk mengiklankan tempat kursus mereka. Salah satu
sekolah musik yang turut bersaing adalah “Melodia Music” yang berlokasi di Jalan Kyai Luhur no. 5. Saat ini, nama Melodia sebagai salah satu sekolah
musik di kota Bandung cukup diminati oleh orang yang ingin belajar musik terutama musik klasik karena didukung dengan ujian Royal salah satu
license dari Amerika yang berstandar sertifikasi cukup tinggi. Hal inilah
yang menarik cukup banyak minat konsumen untuk belajar di sekolah musik tersebut. Walaupun demikian, saat ini Melodia sedang mengalami
penurunan jumlah khususnya kelas piano di tahun 2007-2008 sekitar ± 20- 30. Hal ini dapat dilihat dari jumlah siswa yang lulus pada tiap grade dan
jumlah siswa yang naik di grade selanjutnya serta dari jumlah murid yang keluar di tengah-tengah masa pembelajaran.
Universitas Kristen Maranatha 1-2
1.2 Identifikasi Masalah
Faktor-faktor yang dapat menjadi penyebab berkurangnya jumlah siswa
adalah :
1 Banyaknya sekolah musik yang menjadi pesaing “Melodia Musik”
2 Kurang gencarnya pemasaran yang dilakukan oleh Melodia
3 Kurangnya informasi mengenai kualitas jasa yang ditawarkan Melodia
1.3 Pembatasan Masalah
Mengingat keterbatasan waktu yang ada dalam pengerjaan tugas akhir ini, maka masalah akan dibahas terbatas hanya pada beberapa masalah saja.
Beberapa hal yang menjadi pembatas adalah : • Pengamatan diperuntukkan bagi siswa yang mengikuti kelas piano saja
dan yang masih mengikuti program pendidikan kelas piano mulai dari grade 1 hingga 8.
• Persaingan dengan sekolah musik lain tidak akan dibahas
1.4 Perumusan Masalah
Adapun yang menjadi perumusan masalah adalah sebagai berikut : 1.
Apa yang menjadi kekurangan dan kelebihan melodia di mata konsumen?
2. Faktor – faktor apakah yang dapat membuat seseorang untuk tetap
bertahan untuk belajar musik di Melodia? 3.
Usaha – usaha apa saja yang dapat dilakukan Melodia untuk mempertahankan jumlah siswa khususnya kelas piano?
1.5 Tujuan Penelitian
Yang menjadi tujuan penelitian adalah sebagai berikut : 1.
Dapat mengetahui kelebihan dan kekurangan dari Melodia Musik dan dapat memperbaikinya
2. Mengetahui faktor-faktor yang mempengaruhi keputusan konsumen
untuk belajar di Melodia
Universitas Kristen Maranatha 1-3
3. Untuk mengetahui usaha-usaha apa yang dapat dilakukan Melodia guna
mempertahankan jumlah siswa kelas piano
1.6 Sistematika Penulisan
Sistematika penulisan adalah sebagai berikut : • Bab 1 PENDAHULUAN
Berisi tentang latar belakang permasalahan, identifikasi masalah, pembatasan masalah dan perumusan masalah.
• BAB 2 TINJAUAN PUSTAKA
Berisi tentang teori permasalahan mengenai strategi pemasaran yang akan dibahas berdasarkan Bauran Pemasaran dan berisi tentang teori
mulai dari permasalahan sampai dengan metodologi yang digunakan.
• BAB 3 METODOLOGI PENELITIAN
Pada bab ini akan dibahas teknik langkah-langkah dari penelitian yang akan dilakukan pada penelitian ini.
• BAB 4 PENGUMPULAN DATA
Bab ini berisi tentang data-data mengenai persepsi konsumen
• BAB 5 PENGOLAHAN DAN ANALISIS DATA
Berisi pengolahan data dengan menggunakan faktor analisis serta analisis regresi yang digunakan untuk mencari faktor yang memiliki
pengaruh dengan keputusan konsumen untuk melanjutkan studi di Melodia
• BAB 6 KESIMPULAN DAN SARAN
Bab ini berisi jawaban atas perumusan masalah dan saran untuk
Melodia
6-1
BAB 6 KESIMPULAN DAN SARAN
6.1 Kesimpulan
1. Yang menjadi kekurangan dan kelebihan Melodia :
Dari hasil kuesioner, dapat dilihat bahwa semua responden sudah memiliki persepsi yang baik akan performansi Melodia. Hal ini terbukti
dari banyaknya konsumen yang berpendapat setuju terhadap pernyataan yang diajukan dalam kuesioner sehingga keseluruhan variabel tersebtu
secara tidak langsung dapat menjadi kelebihan di mata konsumen Melodia. Sedangkan kekurangannya tidak terlihat pada penelitian ini.
2. Faktor-faktor yang mempengaruhi seseorang untuk tetap bertahan untuk
melanjutkan studi di Melodia adalah : • Konsumen pernah mengetahui Melodia dari iklan di Media
• Terdapat diskon jika konsumen mengambil paket-paket tertentu. • Pengajar mengajar pada waktu yang telah ditetapkan. Hasil
kuesioner dari variabel ini pun sudah cukup baik. 3.
Usaha-usaha yang dapat dilakukan Melodia untuk mempertahankan jumlah siswa kelas piano adalah :
Pada hasil akhir kuesioner, dapat dilihat bahwa para variabel dirasa sudah cukup baik dan cukup penting oleh para responden. Oleh karena
itu untuk waktu ke depan diharapkan agar Melodia dapat terus mempertahankan performansinya.
Universitas Kristen Maranatha 6-2
6.2 Saran
Adapun cara-cara yang dapat dicoba untuk diterapkan guna meningkatkan jumlah murid kelas piano di Melodia adalah sebagai berikut :
1. Dalam penelitian ini, masih banyak kekurangan. Salah satunya dalam uji
t pada proses regresi linier. Faktor yang mempengaruhi dari 6 factor score
hanya 1 yaitu factor score 4 yang terdiri dari 3 variabel. Secara teori, seharusnya ke-6 factor score tersebut berpengaruh secara
signifikan terhadap keputusan konsumen untuk melanjutkan studi. Hal ini dapat terjadi akibat kekeliruan dalam mengisi kuesioner. Kekeliruan
mungkin dapat disebabkan karena pada waktu penyebaran kuesioner hanya dititipkan pada guru-guru yang bersangkutan atau tidak
mendapatkan pengawasan secara langsung sehingga banyak terjadi kekeliruan persepsi yang tidak dapat dicegah. Oleh sebab itu untuk
penelitian ke depan, mungkin proses pengisian kuesioner dapat dihadiri oleh peneliti.
2. Penelitian sampel yang dilakukan mengambil sampel jenuh. Namun
pada kenyataannya tidak semua siswa telah mengisi kuesioner. Ada yang berhalangan karena sedang cuti atau sedang berada di luar kota.
3. Penelitian seharusnya dilakukan secara spesifik pada tiap grade. Hal ini
dimaksudkan untuk menghindari bias pada hasil dari regresi linier yang mungkin menyebabkan variabel pada analisis regresi tidak signifikan
4. Pada penelitian ini terdapat kelemahan tentang pengisian kuesioner bagi
siswa tingkat akhir grade 8. Pada tingkat ini murid dapat dipastikan tidak akan melanjutkan studi di Melodia karena tidak ada kelas lanjutan
ketika lulus grade 8 ini. 5.
Dari hasil kuesioner tidak ditemukan adanya kekurangan Melodia. Oleh karena itu penurunan jumlah murid kelas piano Melodia mungkin
disebabkan oleh variabel-variabel lain yang tidak dibahas dalam penelitian ini.
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